Man pages for fdavidcl/ruta
Implementation of Unsupervised Neural Architectures

add_weight_decayAdd weight decay to any autoencoder
apply_filterApply filters
as_lossCoercion to ruta_loss
as_networkCoercion to ruta_network
autoencodeAutomatically compute an encoding of a data matrix
autoencoderCreate an autoencoder learner
autoencoder_contractiveCreate a contractive autoencoder
autoencoder_denoisingCreate a denoising autoencoder
autoencoder_robustCreate a robust autoencoder
autoencoder_sparseSparse autoencoder
autoencoder_variationalBuild a variational autoencoder
contractionContractive loss
correntropyCorrentropy loss
decodeRetrieve decoding of encoded data
denseCreate a fully-connected neural layer
dropoutDropout layer
encodeRetrieve encoding of data
encoding_indexGet the index of the encoding
evaluateEvaluation metrics
evaluation_metricCustom evaluation metrics
generateGenerate samples from a generative model
inputCreate an input layer
is_contractiveDetect whether an autoencoder is contractive
is_denoisingDetect whether an autoencoder is denoising
is_robustDetect whether an autoencoder is robust
is_sparseDetect whether an autoencoder is sparse
is_trainedDetect trained models
is_variationalDetect whether an autoencoder is variational
join-networksAdd layers to a network/Join networks
layer_kerasCustom layer from Keras
loss_variationalVariational loss
make_contractiveAdd contractive behavior to any autoencoder
make_denoisingAdd denoising behavior to any autoencoder
make_robustAdd robust behavior to any autoencoder
make_sparseAdd sparsity regularization to an autoencoder
new_autoencoderCreate an autoencoder learner
new_layerLayer wrapper constructor
new_networkSequential network constructor
noiseNoise generator
noise_cauchyAdditive Cauchy noise
noise_gaussianAdditive Gaussian noise
noise_onesFilter to add ones noise
noise_saltpepperFilter to add salt-and-pepper noise
noise_zerosFilter to add zero noise
outputCreate an output layer
plot.ruta_networkDraw a neural network
print-methodsInspect Ruta objects
reconstructRetrieve reconstructions for input data
save_asSave and load Ruta models
sparsitySparsity regularization
sub-.ruta_networkAccess subnetworks of a network
to_kerasConvert a Ruta object onto Keras objects and functions
to_keras.ruta_autoencoderExtract Keras models from an autoencoder wrapper
to_keras.ruta_layer_inputConvert Ruta layers onto Keras layers
to_keras.ruta_layer_variationalObtain a Keras block of layers for the variational...
to_keras.ruta_loss_namedObtain a Keras loss
to_keras.ruta_networkBuild a Keras network
to_keras.ruta_sparsityTranslate sparsity regularization to Keras regularizer
to_keras.ruta_weight_decayObtain a Keras weight decay
train.ruta_autoencoderTrain a learner object with data
variational_blockCreate a variational block of layers
weight_decayWeight decay
fdavidcl/ruta documentation built on May 8, 2018, 11:16 a.m.